Tag Archives: methodology

New Census definition of poverty behind the rise of poverty in the US?

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While media outlets have spread the recent news from the Census Bureau that poverty has increased in the United States, some conservatives question whether this is a true change or reflects a change in the measurement of poverty:

The new Census measure suggests that the ranks of the poor – at 49 million – are 3 million larger than previously thought. The increase comes in the new way poverty is measured. The new Census report for the first time includes government subsidies and benefits such as food stamps as a part of household income, but it also factors in rising costs, such as health-care expenses. The result creates a new poverty line and a new view of who in the US is poor.

The new threshold for poverty for family of four, for example, is $24,343, as opposed to $22,113. And the revision reveals greater poverty trends among Asians, Hispanics, whites, and the elderly, and declining poverty for blacks and children, who tend to be greater beneficiaries of food stamps…

Sociologists say the new numbers give greater nuance to the portrait of poverty in the US, highlighting the degree to which government programs are keeping struggling Americans afloat. Critics counter the numbers are engineered precisely to make government assistance appear indispensable and to pave the way for a broader redistribution of American wealth toward the poor…

The Census changes are the first revisions to how the poverty rate is calculated since 1963. Since then, it has been gauged solely by cash income per household. But the new figures give a larger sense of what impact government spending has on poverty, says Timothy Smeeding, an economist at the University of Wisconsin in Madison.

Can’t really say I’m surprised that these figures are politicized. But, then again, the measurement of poverty has been a contentious topic for decades.

Measuring how much the Internet is worth: $8 trillion?

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A recent report by McKinsey puts the value of the Internet at $8 trillion. Here are a few other fun facts:

There is a lot of Internet to measure, with two billion global consumers and $8 trillion in total revenue. So McKinsey’s report limited its scope to the online economy in the G-8 countries plus five more: Brazil, China, India, South Korea and Brazil. It defined Internet activities as private consumption (electronic equipment, e-commerce, broadband subscriptions, mobile Internet, and hardware and software consumption); private investment (from the telecommunications industry and the maintenance of extranet, intranet, and Web sites); public expenditure (spending and buying by government in software hardware and services); and trade (which accounts for exports of Internet equipment plus business-to-business services with overseas companies)…

As an industry, the Internet contributes more to the typical developed economy than mining, utilities, agriculture, or education. In Sweden, fully one-third of economic growth in the five years leading up to the recession came from Internet activities. For the entire G-8, the average was 21 percent. In an analysis of France since the mid-1990s, McKinsey found that the Internet created more than twice the number of jobs it destroyed.

Much of the Internet’s contribution to our lives is nearly impossible to measure. For example, I use email. How much is that worth to me? I can’t even begin to say. I read hundreds of news sources a day. What is that worth to me, or to the news organizations? Pricing this kind of thing is exhausting to think about. But since analyzing what the rest of us find “exhausting to think about” is McKinsey’s job, their researchers looked at the “consumer surplus” of the Internet, concluding that the total annual benefit to the United States comes out to $64 billion…

The United States is the world leader in the online industry, grabbing 30 percent of global Internet revenues. But the UK is the world leader in online retail. The British spent $2,535 on e-stuff in 2009, more than twice the average of the world’s largest countries and still 1.4 times the amount of the typical U.S. shopper. Sweden leads the world in Internet’s contribution to GDP. Fully 6.3 of the country’s economy is online — twice Germany, France or India. In Russia, the Internet contributes not even one percent of GDP.

Some interesting stuff here:

1. I appreciate the emphasis on the difficulty of measuring this topic. In addition to simply thinking about the economic benefits, we could spend a lot of time discussing how it has altered social interaction, private practices, and democracy. I wonder what the margin of error is on the estimates.

2. There is some indication of the splits between the Internet haves and have-nots. If the Internet is so valuable, should this be a leading component of aid to poorer countries? It does require a decent investment in infrastructure but it would allow people to easily connect to first-world countries and industries. For example, what is the impact of the less than $100 laptop that was touted for years?

3. With all of this money (and value floating around), it is a reminder why so many states want to get their hands on sales tax revenues from Internet sales. Do European countries like Britain have a similar system? I have bought a few things from Amazon.co.uk in the past and I don’t recall the experience being much different.

4. I would be interested to know the future prospects for the Internet’s growth: how quickly will it grow? How much will it expand? Is most of the growth within developed countries or in opening or expanding newer markets (China and India plus others)?

Disagreement on whether there are 7 billion people on earth just yet

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There have been a number of recent stories about how the world’s population has reached 7 billion. Interestingly, not everyone agrees that this has happened yet:

According to United Nations demographers, 6,999,999,999 other Earthlings potentially felt the same way on Monday when the world’s population topped seven billion. But if you’d rather go by the United States Census Bureau’s projections, you’ve got some breathing room. The bureau estimates that even with the world’s population increasing by 215,120 a day, it won’t reach seven billion for about four months.

How do the dueling demographic experts reconcile a difference, as of Monday, of 28 million, which is more than all the people in Saudi Arabia?

They don’t.

“No one can know the exact number of people on the globe,” Gerhard Heilig, chief of the population estimates and projections section of the United Nations Population Division, acknowledges.

Even the best individual government censuses have a margin of error of at least 1 percent, he said, which would translate in the global aggregation to “a window of uncertainty of six months before or six months after Oct. 31.” An error margin of even as little as 2 percent would mean that Monday’s estimate of seven billion actually was 56 million off (which is more people than were counted in South Africa).

Figuring this out is not an easy task. It requires a central group to tabulate results from all of the countries around the world. Could there be a difference in the reliability and validity of the results across nations? For example, can we trust population counts from honed operations in the United States and other Western nations more than counts from Third World countries? (I wish the article went into this: how accurate are population figures from different countries? How big might the margins of errors be?) I’ve seen this before when doing some research in graduate school on suicide figures that the United Nations has collected – in the period I was looking at, roughly 1950 to 1970, some countries didn’t report, some had rougher estimates, and countries could have different definitions about what constitutes a suicide. Absolute population counts should be more straight forward but I imagine there could be a number of complications.

Will we get another round of news stories when the Census Bureau says we have hit 7 billion? I wonder if the perceived global authority of the United Nations versus that of the Census Bureau plays a role. For example, did the New York Times report the 7 billion figure as front-page news and then print this caveat story later in the news section?

A final note: the story ends by suggesting the two estimates are not that far off. If we could be so lucky that all of our estimates have only a 1% margin of error, science would benefit greatly. But it is a reminder that official figures are estimates, not 100% counts of social phenomenon.

Looking for a new area of study? Try Twitterology

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If it is in the New York Times, Twitterology must be a viable area of academic study:

Twitter is many things to many people, but lately it has been a gold mine for scholars in fields like linguistics, sociology and psychology who are looking for real-time language data to analyze.

Twitter’s appeal to researchers is its immediacy — and its immensity. Instead of relying on questionnaires and other laborious and time-consuming methods of data collection, social scientists can simply take advantage of Twitter’s stream to eavesdrop on a virtually limitless array of language in action…

One criticism of “sentiment analysis,” as such research is known, is that it takes a naïve view of emotional states, assuming that personal moods can simply be divined from word selection. This might seem particularly perilous on a medium like Twitter, where sarcasm and other playful uses of language often subvert the surface meaning…

Still, the Twitterologists will continue to have a tough row to hoe in justifying their research to those who think that Twitter is a trivial form of communication. No less a figure than Noam Chomsky has taken Twitter to task recently for its “superficiality.”

For more sociological thoughts about Chomsky’s comments, see this post from a few days ago.

Here is my quick take on Twitterology: it has some potential for gathering quick, on-the-ground information. But there are two big issues that this article doesn’t address:

1. Are Twitter users representative of the whole population? Probably not. Twitter feeds might be good for studying very specific groups and movements.

2. How can one make causal arguments with Twitter data? If we had more information about Twitter users from profiles, this might be doable but Twitter is less about Facebook-style profiles. We then need studies that collect the information about Twitter users as well as their Twitter activity. If we want to ask questions like whether Twitter was instrumental or even helped cause the Arab Spring movements, we need more data.

Twitterology may be trendy at the moment but I think it has a ways to go before we can use it to tackle typical questions that sociologists ask.

“What’s Your Problem?” misses an opportunity to explain survey research

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The “What’s Your Problem?” column in the Chicago Tribune tackles the problems of consumers. Yesterday’s column involved a woman who had been called multiple times by a survey firm even after she asked to not be called again:

Over the following weeks, Scarborough representatives called Riedell repeatedly, asking her to participate in a 15-minute phone survey.

No matter how many times she refused their overtures, the calls kept coming.

Riedell said she asked each time to have her name taken off the call list but was told that representatives were not authorized to do so.

And so it continued through late summer and early fall. By the sixth call, Riedell decided she had heard enough. She emailed What’s Your Problem?

When contacted by the Tribune, the survey firm had this reponse which did not please Riedell:

Dercher said Riedell did not leave her name and phone number when she called Scarborough’s toll-free number, which are critical pieces of information so that the company can remove a respondent from the calling list.

Although her number could be randomly picked for another survey in the future, the odds are against that happening, Dercher said.

After reading Dercher’s email, Riedell said Scarborough’s response was, well, lame.

“The response says that their interviewers are not allowed to remove the name of a respondent from their calling list since the respondent’s name is confidential, but the interviewer already has the respondent’s name and phone number, otherwise they wouldn’t have been able to reach me by phone or address me by my name when I answered the phone,” Riedell said. “Sounds like gibberish to me.”

The column is clearly geared toward Riedell’s point of view and frankly, who likes to be called repeatedly by companies or survey organizations after refusing to participate? At the same time, let’s flip this around to see it from the opposite angle:

-Riedell was selected for the survey by random digit dialing. This is not unusual and telephone surveys are not covered by the Do Not Call registry.

-It doesn’t sound unusual that the survey interviewers didn’t have the power to remove her name from their lists. They were likely handed lists of numbers and told to call until they had an answer.

-Surveys often select their initial batch of respondents and then do whatever they can to get responses from them. The US Census Bureau goes to housing units repeated times in order to collect data because they want accurate data. (Of course, one Census worker who was doing his job last year was arrested for trespassing in Hawaii.) If survey companies simply gave up on people after one attempt, they would spend a lot more time and money and doing so might mess up their calibrated samples which are meant to represent larger populations.

In the end, Riedell may not like the system but in order to collect good data, survey companies may have to contact selected respondents multiple times. Since participation is voluntary, Riedell can opt out and perhaps Scarborough does need to have a more clearly delineated method by which people can opt out. Additionally, there may be some complications because Scarborough is a market survey research firm (tagline: Scarborough Research measures our shopping, media, and lifestyle behaviors) and are not academic researchers or political researchers (though push polls are very problematic). But this column could be much more informative about how survey research works and how consumers can respond to common requests for information rather than just suggesting that this woman should be able to more easily avoid telephone survey questions.

Cities ranked by the “Trick or Treat Index”

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Richard Florida has put some of his data to use to answer an important question: what are the best cities in the united States for trick or treating on Halloween?

According to National Retail Federation projections, Americans will spend $6.86 billion on Halloween this year, up from $3.3 billion in 2005 when a lot fewer of us were out of work. But even as Halloween edges up on Christmas as a shopping opportunity, the trick-or-treating experience is a lot less universal than it was. In some towns, you see hardly any unsupervised trick-or-treaters after dark; in other places—Brooklyn Heights or my neighborhood in Toronto leap to mind—there are more kids than you can imagine.

Herewith the 2011 edition of the Trick-or-Treater Index developed with the ever-able number-crunching of my Martin Prosperity Institute colleague, Charlotta Mellander. The 2011 Index is based on the following five metrics: the share of children aged 5 to 14; median household income (figuring the haul will be better in more affluent metros), population density, walkability (measured as the percentage of people who walk or bike to work) and creative spirit (which we measured as the percentage of artists, designers, and other cultural creatives). The data are from the American Community Survey and cover all U.S. metro areas, both their cores and suburbs…

As for the top ranking metros, Bridgeport-Stamford-Norwalk, Connecticut, comes in first again this year. Greater New York has moved up to second place, followed by Chicago, greater Washington, D.C., and the twin cities of Minneapolis-St. Paul. Los Angeles, last year’s runner-up, has dropped to 7th place. Big metros dominate the top spots, but Lancaster, Pennsylvania, has moved all the way up from 16th last year to 6th on our 2011 rankings. And college towns like Ann Arbor, Michigan, Boulder, Colorado, and New Haven, Connecticut, also rank among the top 25.

This reminds me of another recent odd use of data that ranked the luckiest cities. So people with more money, who are more creative, and live in more walkable areas necessarily give more or better candy? Might they also be the people who are more likely to give substitutes to candy? Could this also be related to health measures, like obesity or life expectancy? This seems like opportunistic, atheoretical data mining meant to get a few page clicks (like me).

And since there are probably few people who would go to a whole new metropolitan area just to get candy, wouldn’t this be a better analysis if it was at a zip code, community, or census block level?

How to rank the luckiest cities in the United States

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Perhaps we have taken these rankings lists too far: Men’s Health has ranked the luckiest cities in the United States.

Luck is like that dark matter stuff scientists have spent billions of dollars trying to find with the Large Hadron Collider—a powerful presence that people surmise exists but no one has actually seen. The difference is that we found luck. Using statistics instead of protons, we pinpointed the location of a large supply in, of all places, San Diego.

Wondering how Vegas didn’t hit this jackpot? Here’s our definition of good luck: the most winners of Powerball, Mega Millions, and Publishers Clearing House sweepstakes; most hole-in-ones (PGA); fewest lightning strikes (including the fatal kind) and deaths from falling objects (Vaisala Inc., National Climatic Data Center, CDC); and least money lost on lottery tickets and race betting (Bureau of Labor Statistics).

San Diego is number one on the list with Baltimore, Phoenix, Wilmington (Delaware), and Richmond rounding out the top five. Chicago is #36. The bottom five: Sioux Falls, Memphis, Jackson (Mississippi), Tampa, and Charleston (West Virginia).

What I like about this is that they are straightforward with what factors went into the rankings (though they might have been weighted). These are what we might consider “very rare” and cultural conditioned lucky events. The lottery is perhaps the poster child for this. If someone wins more than once, some suspicions might surface (see a story about a four-time Texas winner here). What about lesser luck, such as avoiding a car accident at the last minute or local sports teams coming up with miraculous plays at the end of a game or avoiding natural disasters? Such things would be much more difficult to measure and it might always be an open statistical question of whether strange occurrences could be explained by some other unmeasured or unknown factor.

Should anyone move to the luckier cities to really improve their chances? No, the statistical odds of any of these things happening is still quite small. In fact, it would be interesting to see how much really separates the luckiest cities from the unluckiest – are we talking a difference of 1 in a million? Ten in a million?

In the end, I think these rankings don’t really tell us much about anything. People shouldn’t use them as a guide and measuring luck is fraught with difficulty. Take the lottery winnings: could this simply reflect the fact that people in certain cities buy more tickets or their states have bigger lottery jackpots which encourages more participation? This is a story that uses real numbers to make a nebulous point in order to gain website clicks (guilty as charged) and sell magazines.

Sociologist tells how time diaries provide six insights in the study of national well-being

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An Oxford sociologist gives six findings regarding national well-being based on time diary data.

Time diaries allow researchers to get at daily activities and move past some of the memory and social desirability issues that come up in interviews or surveys.

Sociologists tracking “global mood swings” through Twitter

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New social media platforms like Facebook and Twitter are ripe data sources. A new study in Science done by two sociologists examines the world’s emotions through Twitter:

The research team, led by Scott Golder, a PhD doctoral student in the field of sociology, and Professor of Sociology Michael Macy, tracked 2.4 million people in 84 different countries over the past two years. Clearly the team working on the project didn’t read through 2.4 million people’s tweets. Instead, they used a text analysis program that quantified the emotional content of 509 million tweets. Their results, featured in the paper “Diurnal and Seasonal Mood Tracks Work, Sleep and Day Length Across Diverse Cultures,” were published September 29 in Science.

The researchers found that work, sleep, and the amount of daylight we get really does affect things like our enthusiasm, delight, alertness, distress, fear, and anger. They concluded that people tweet more positive things early in the morning and then again around midnight. This could suggest that people aren’t very happy while they’re working since their happy tweets are at the beginning and end of the day. Saturday and Sunday also saw more positive tweets in general. The weekend showed these peaks at about 2 hours later, which accounts for sleeping in and staying out late.

Of course, all of the trends weren’t the same throughout every country. For example, the United Arab Emirates tend to work Sunday through Thursday, so their weekend tweets happened on Friday and Saturdays. The results also found that people who live in countries that get more daylight (closer to the equator) aren’t necessarily happier than people in countries that get less daylight (closer to the North and South Poles). It seems that only people who have a lot of daylight during the summer and then very little in the winter feel the affect of the change in seasons as much.

Clearly the results of the research aren’t perfect. There may be some people who only share positive things on Twitter, or some people who love to be cynical and use Twitter to complain about problems.

This sounds interesting and the resulting maps and charts are intriguing.  However, I would first ask methodological questions that would get at whether this is worthwhile data or not. Does this really reflect global moods? Or does this simply tell us something about Twitter users, who are likely not representative of the population at large?

Another article does suggest this study makes methodological improvements over two common ways studies look at emotions:

None of these results are particularly surprising, but Golder and Macy suggest that using global tweets allows them to confirm previous studies that only looked at small samples of American undergraduates who were not necessarily representative of the wider world. Traditional studies also require participants to recall their past emotions, whereas tweets can be gathered in real time.

These are good things: more immediate data and a wider sample beyond college undergraduates. But this doesn’t necessarily mean that the Twitter data is good data. The sample still probably skews toward younger people and those who have the technological means to be on Twitter consistently. Additionally, immediate emotions can tell us one thing but inquiring about longer-term satisfaction often tells us something else.

On the whole, this sounds like better data than we have before but until we have more universal Twitter usage, this data source will have significant limitations.

Crowd Counting 101

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Every now and then, often connected to politically contentious events like the “Restoring Honor” or “Rally to Restore Sanity” in 2010 or Egyptians taking to the streets in early 2011, you will see articles about how officials and media sources estimate the number of people who attend. Here is a primer on crowd counting. Some of the possible new methods could help give us accurate and not politically-driven counts:

And, as Yip said in a statement about his study, a good way to count crowds could cut through the politically motivated stats we put up with now. “In the absence of any accurate estimation methods, the public are left with a view of the truth colored by the beliefs of the people making the estimates. The public would be better served by estimates less open to political bias.”

I look forward to improved crowd counting.

h/t Instapundit